Before testing starts, Sybil builds a model of your application: purpose, tech stack, and full attack surface, authenticated and unauthenticated. You can see this map yourself, so your team knows exactly what's exposed and what's been tested.

A hierarchy of specialized agents tests hypotheses across your applications, each owning a domain like auth, business logic, or injection. The edge is chaining: combining findings into real exploit paths the way a human attacker would.

Every finding passes a multi-stage validation pipeline: deduplication, reproducibility, false-positive triage. Nothing surfaces until it's reproduced independently.

Each finding includes detailed AI-generated remediation recommendations, plus a "Fix with AI" flow that builds a ready-to-run prompt (or CLI command for Codex, Claude Code, or Cursor) pointing your coding assistant at the RunSybil MCP server to pull finding details and auto-remediate locally. Trigger a retest and an automated validator agent re-runs the original attack to confirm the vulnerability is actually fixed and not just returning a different error. Findings and reports track remediation status end-to-end, so you can see open vs. remediated issues across the full engagement.

After the initial engagement, Sybil tests only what changed, triggered by a PR, a manual run, or a set cadence, informed by everything it already knows about your application. That's what makes ongoing coverage viable at scale, instead of a scanner re-running blindly on a timer.